Authentication mechanisms use one or more authentication factors to control access to secured information. An authentication mechanism may require a knowledge factor (e.g., a username and a password), an ownership factor (e.g., a hardware security token), an inherence factor (e.g., a biometric identifier such as a fingerprint), or combinations thereof. Thus, for example, authentication of a user on a web portal can involve “what you know,” “what you have,” and “who you are.” The first of these is commonly referred to as proof of knowledge.
Authentication based on proof of knowledge includes an enrollment phase to define user knowledge and a use phase to authenticate a user that proves that knowledge. A non-limiting example of proof of knowledge is a technology known as “picture password” (see, e.g., Wayne Jansen et al., “Picture Password: A Visual Login Technique for Mobile Devices,” National Institute of Standards and Technology, NISTIR 7030, July 2003). Similar picture password functionality has been incorporated into WINDOWS® 8 for logging into the WINDOWS® 8 operating system. A picture password includes a combination of one or more input actions associated with a known image (e.g., without limitation, a still picture, a motion picture with or without sound, a photograph, or the like) that may be used to authenticate a user who is able to prove his knowledge by repeating the input actions in conjunction with the image. Another non-limiting example of a proof of knowledge that uses a “picture password” is disclosed by U.S. Pat. No. 8,813,183 entitled METHOD AND SYSTEM FOR PROCESSOR OR WEB LOGON, which is incorporated herein by reference in its entirety. U.S. Pat. No. 8,813,183 discloses the use of an image and knowledge about that image that a user can readily remember for authentication.
Picture passwords can replace or supplement conventional passwords as proofs of knowledge. For example and without limitation, picture passwords can be used for web logins to access web accounts (e.g., without limitation, a bank account, a brokerage account, electronic billing, or a payment system). Thus, a picture password can replace a textual password, Personal Identification Number (PIN), or pass phrase (i.e., conventional passwords). A username is typically associated with any proof of knowledge because it is possible to have a non-unique conventional password. Although a picture password may be more unique than other conventional passwords, a unique username may still be required by a Relying Party (RP) (e.g., the bank providing the bank account, the brokerage firm providing the brokerage account, or the proprietor of the electronic billing or payment system) to ensure security.
Authentication mechanisms may also distinguish between human and machine input. Systems such as Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) provide a type of challenge-response test used in computing to determine whether or not a user is a human, instead of, for example, a “robot” or other type of computer agent seeking to thwart an authentication mechanism. A CAPTCHA protects websites against robots by generating and grading tests that humans can pass but current computer programs cannot. For example, human input is authenticated by displaying distorted text that only a human user can perceive and input.
It is believed that text-based passwords or picture passwords do not provide protections similar to those provided by CAPTCHA. However, picture password technology has a number of advantages over text-based passwords, including more entropy for fewer actions. Entropy refers to a lack of predictability of input actions and provides a measure of the strength of proof of knowledge.
Picture passwords have greater entropy because their corresponding vocabulary is larger than textual passwords that rely on a combination of characters. Vocabulary refers to the range of inputs used for authentication. Much of the difference in vocabulary size over text-based passwords results from a wide range of possible unique input actions over a range of locations on a unique image.
To prove user knowledge without actually recording a password, it is desirable to store a picture password as a cryptographic hash of input actions associated with an image. This almost always involves discretizing a displayed image into a grid that has multiple sections by using, for example, various tessellation techniques (e.g., without limitation, rectangular grids, hexagonal grids, or Voronoi tessellations). Further, vocabulary size for picture passwords decreases as error tolerance increases due to placement of the grid and tolerance of inputs at adjacent grid locations (e.g., without limitation, a tolerance of 9 for rectangular grids or 7 for hexagonal grids).
The use of touch-sensitive surfaces as input devices for electronic devices has increased significantly in recent years. As such, touch-sensitive surfaces are widely used as input devices to authenticate users. Examples of touch-sensitive surfaces include touch pads and touch screen displays.
For portable and/or handheld electronic devices with relatively small display screens, existing picture password methods are cumbersome, inefficient, and inaccurate. For example, a portable handheld device with a small screen (e.g., smartphones and other pocket-sized devices) displays a relatively small image for a picture password but still requires a high degree of accuracy to authenticate a user.
Current picture password systems may be fashioned by selecting a number of element(s) associated with an image in response to a gesture such as a stroke(s) that form drag paths on an image, including stroke(s) that form drag paths that avoid previous stroke(s) on the image. This situation creates a significant cognitive burden on a user that may have difficulty seeing and inputting actions on small display devices with sufficient accuracy to authenticate the user.
Thus, there is a problem when going from a relatively larger picture display to a relatively smaller picture display such as, for example and without limitation, going from a laptop display screen to a relatively smaller smartphone touch-sensitive display, while trying to preserve the entropy advantage of picture passwords. An input object such as a user's finger or stylus can be cumbersome to touch, swipe, or move along a path with sufficient precision to correctly select grid locations required for a picture password. In addition, users would prefer to use the same picture password created, for example, with a mouse on a high resolution display on a relatively smaller touch-sensitive display of a smartphone.
With the wide range of available display sizes (e.g., without limitation, desktop, laptop, tablet, smartphone, or wearable display screens) and the various types of pointer (e.g., cursor) interactions (e.g., without limitation, caused by a mouse, a gesture on a touch-sensitive display, or a “hoverscreen” with which a user can hover a hand or finger a distance away from the display surface and cause an action, such as flipping through an e-book or surfing the web, without causing a finger smudge), there is a need for a user input mechanism that allows the same picture password activity to be detected across all possible display sizes and cursor interaction types in a way that is seamless to a user. Accordingly, a need exists for devices, systems, and methods that employ picture passwords on relatively small display screens.